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How can data be imported to predict the fuel efficiency with Auto MPG dataset (basic regression) using TensorFlow?
Tensorflow is a machine learning framework that is provided by Google. It is an open-source framework used in conjunction with Python to implement algorithms, deep learning applications and much more. It is used in research and for production purposes.
The ‘tensorflow’ package can be installed on Windows using the below line of code −
pip install tensorflow
Tensor is a data structure used in TensorFlow. It helps connect edges in a flow diagram. This flow diagram is known as the ‘Data flow graph’. Tensors are nothing but multidimensional array or a list.
The aim behind a regression problem is to predict the output of a continuous or discrete variable, such as a price, probability, whether it would rain or not and so on.
The dataset we use is called the ‘Auto MPG’ dataset. It contains fuel efficiency of 1970s and 1980s automobiles. It includes attributes like weight, horsepower, displacement, and so on. With this, we need to predict the fuel efficiency of specific vehicles.
We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.
Following is the code to predict the fuel efficiency with Auto MPG dataset −
Example
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
np.set_printoptions(precision=3, suppress=True)
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
print("The version of tensorflow is ")
print(tf.__version__)
url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'
column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration', 'Model Year', 'Origin']
print("The data is being loaded")
print("The column names have been defined")
raw_dataset = pd.read_csv(url, names=column_names, na_values='?', comment='\t', sep=' ', skipinitialspace=True)
dataset = raw_dataset.copy()
print("A sample of the dataset")
dataset.head(2)Code credit − https://www.tensorflow.org/tutorials/keras/regression
Output
The version of tensorflow is 2.4.0 The data is being loaded The column names have been defined A sample of the dataset
| sl. | MPG | Cylinders | Displacement | horsepower | weight | Acceleration | Model Year | Origin |
|---|---|---|---|---|---|---|---|---|
| 0 | 18.0 | 8 | 307.0 | 130.0 | 3504.0 | 12.0 | 70 | 1 |
| 1 | 15.0 | 8 | 350.0 | 165.0 | 3693.0 | 11.5 | 70 | 1 |
Explanation
The required packages are imported and aliased.
The data is loaded, and column names are defined for it.
A sample of the dataset is displayed on the console.
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